Contata’s knowledge mining services utilize a variety of machine-learning and data mining techniques to support business use cases such as:
Analyzing and learning from existing data to create models that can be used to predict different values of interest – such as categorizing customers for churn, and predicting expected revenues based on existing conditions.
Clustering and Segmentation
Analyzing data to discover naturally occurring groups of individuals, to be used, for example, in better targeting marketing campaigns, or even designing new products.
Profiling and Outlier Analysis
Analyzing data to create profiles of individuals in different segments, to use, for example, in pinpointing out of the ordinary behavior outliers which may indicate fraudulent activity.
Correlation and Causal Analysis
Analyzing data to determine underlying factors that explain correlation between several attributes, or establish a causation relationship between them.